{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "718464bc-d3bc-4231-ae00-2a3bff2fab68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import xgboost as xgb\n",
    "import seaborn as sns \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "np.int = int\n",
    "import matplotlib.pyplot as plt\n",
    "#导入各种用于评估模型性能和参数优化的函数和类，例如均方误差（MSE）、均绝对误差（MAE）、决定系数（R2 Score）、解释方差分（explained variance score）等\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score,explained_variance_score,mean_absolute_percentage_error,mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "#导入XGBoost库中的XGBClassifier类，用于建立XGBoost分类模型，并导入plot_importance函数用于特征重要性可视化。\n",
    "from xgboost import XGBClassifier, plot_importance\n",
    "#导入互信息函数\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "#导入lasso模型筛选特征变量\n",
    "from sklearn.linear_model import Lasso\n",
    "#导入Scikit-Optimize库中的贝叶斯优化相关的函数和类，用于超参数调优\n",
    "from skopt import gp_minimize\n",
    "from skopt.space import Real, Integer\n",
    "#导入交叉验证函数，用于评估模型性能\n",
    "from sklearn.model_selection import cross_val_score\n",
    "#导入随机森林回归模型\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "plt.rcParams['font.family'] = 'Times New Roman'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50045870-09f1-4bd7-b236-4bafb6c90da6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#df = pd.read_excel('指标.xlsx')\n",
    "d = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Feature.xlsx')\n",
    "dd = pd.read_excel(r'D:\\work\\暑期工作\\0606_重新处理\\河南省小麦产量预测\\数据\\Features.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc42cdc5-0483-4439-98af-3593081eff03",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "d=d.drop(columns=['Unnamed: 0'])\n",
    "dd=dd.drop(columns=['Unnamed: 0'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59285029-518e-41c4-8d69-a0f2c2da0cba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NAME</th>\n",
       "      <th>AvgSurfT_inst_chengshu</th>\n",
       "      <th>LST_Day_1km_chousui</th>\n",
       "      <th>LST_Night_1km_fennie</th>\n",
       "      <th>soil_temperature_level_2_kaihua</th>\n",
       "      <th>soil_temperature_level_2_guanjiang</th>\n",
       "      <th>soil_temperature_level_2_shouhuo</th>\n",
       "      <th>soil_temperature_level_3_chousui</th>\n",
       "      <th>soil_temperature_level_3_kaihua</th>\n",
       "      <th>soil_temperature_level_3_guanjiang</th>\n",
       "      <th>...</th>\n",
       "      <th>SVAI_chousui</th>\n",
       "      <th>SVAI_kaihua</th>\n",
       "      <th>WDRVI_bajie</th>\n",
       "      <th>WDRVI_chousui</th>\n",
       "      <th>WDVI_bajie</th>\n",
       "      <th>WDVI_yunsui</th>\n",
       "      <th>WDVI_chousui</th>\n",
       "      <th>WDVI_kaihua</th>\n",
       "      <th>wet_kaihua</th>\n",
       "      <th>亩产</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>410522</td>\n",
       "      <td>301.830072</td>\n",
       "      <td>14770.448120</td>\n",
       "      <td>13653.260039</td>\n",
       "      <td>291.679432</td>\n",
       "      <td>294.485981</td>\n",
       "      <td>301.239969</td>\n",
       "      <td>287.263466</td>\n",
       "      <td>288.780851</td>\n",
       "      <td>290.517675</td>\n",
       "      <td>...</td>\n",
       "      <td>0.509428</td>\n",
       "      <td>0.502203</td>\n",
       "      <td>-0.270294</td>\n",
       "      <td>0.038094</td>\n",
       "      <td>0.312649</td>\n",
       "      <td>0.346212</td>\n",
       "      <td>0.380784</td>\n",
       "      <td>0.355850</td>\n",
       "      <td>-0.090609</td>\n",
       "      <td>7467.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>410421</td>\n",
       "      <td>301.881276</td>\n",
       "      <td>14811.998245</td>\n",
       "      <td>13724.830174</td>\n",
       "      <td>291.624652</td>\n",
       "      <td>294.462918</td>\n",
       "      <td>301.229017</td>\n",
       "      <td>287.616328</td>\n",
       "      <td>289.032764</td>\n",
       "      <td>290.784441</td>\n",
       "      <td>...</td>\n",
       "      <td>0.456349</td>\n",
       "      <td>0.444113</td>\n",
       "      <td>-0.220879</td>\n",
       "      <td>0.020097</td>\n",
       "      <td>0.272293</td>\n",
       "      <td>0.321097</td>\n",
       "      <td>0.313054</td>\n",
       "      <td>0.308834</td>\n",
       "      <td>-0.088144</td>\n",
       "      <td>5972.666001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>411726</td>\n",
       "      <td>298.990822</td>\n",
       "      <td>15035.873561</td>\n",
       "      <td>13760.252546</td>\n",
       "      <td>291.141219</td>\n",
       "      <td>293.187955</td>\n",
       "      <td>298.777454</td>\n",
       "      <td>287.204259</td>\n",
       "      <td>288.763693</td>\n",
       "      <td>290.271875</td>\n",
       "      <td>...</td>\n",
       "      <td>0.375751</td>\n",
       "      <td>0.406768</td>\n",
       "      <td>-0.273682</td>\n",
       "      <td>-0.176015</td>\n",
       "      <td>0.255247</td>\n",
       "      <td>0.291260</td>\n",
       "      <td>0.276982</td>\n",
       "      <td>0.277341</td>\n",
       "      <td>-0.107184</td>\n",
       "      <td>5687.791498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>410822</td>\n",
       "      <td>302.326105</td>\n",
       "      <td>14909.855104</td>\n",
       "      <td>13684.031442</td>\n",
       "      <td>291.393057</td>\n",
       "      <td>294.128413</td>\n",
       "      <td>300.920627</td>\n",
       "      <td>286.732262</td>\n",
       "      <td>288.209019</td>\n",
       "      <td>290.136585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.466787</td>\n",
       "      <td>0.392344</td>\n",
       "      <td>-0.305916</td>\n",
       "      <td>0.009030</td>\n",
       "      <td>0.277992</td>\n",
       "      <td>0.313744</td>\n",
       "      <td>0.335973</td>\n",
       "      <td>0.327689</td>\n",
       "      <td>-0.062063</td>\n",
       "      <td>7959.597222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>411082</td>\n",
       "      <td>302.665974</td>\n",
       "      <td>14764.439462</td>\n",
       "      <td>13685.820730</td>\n",
       "      <td>292.812644</td>\n",
       "      <td>295.863324</td>\n",
       "      <td>302.456445</td>\n",
       "      <td>288.378758</td>\n",
       "      <td>289.948855</td>\n",
       "      <td>291.813767</td>\n",
       "      <td>...</td>\n",
       "      <td>0.479437</td>\n",
       "      <td>0.456340</td>\n",
       "      <td>-0.155182</td>\n",
       "      <td>0.027083</td>\n",
       "      <td>0.329961</td>\n",
       "      <td>0.356682</td>\n",
       "      <td>0.342203</td>\n",
       "      <td>0.353976</td>\n",
       "      <td>-0.074036</td>\n",
       "      <td>7662.496889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>411081</td>\n",
       "      <td>302.379726</td>\n",
       "      <td>14864.959928</td>\n",
       "      <td>13716.550674</td>\n",
       "      <td>292.036420</td>\n",
       "      <td>294.927847</td>\n",
       "      <td>301.419777</td>\n",
       "      <td>287.740673</td>\n",
       "      <td>289.256381</td>\n",
       "      <td>291.034992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438036</td>\n",
       "      <td>0.427608</td>\n",
       "      <td>-0.222630</td>\n",
       "      <td>-0.033628</td>\n",
       "      <td>0.276777</td>\n",
       "      <td>0.307460</td>\n",
       "      <td>0.306785</td>\n",
       "      <td>0.308283</td>\n",
       "      <td>-0.090942</td>\n",
       "      <td>6613.422500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>411424</td>\n",
       "      <td>302.404313</td>\n",
       "      <td>14767.178119</td>\n",
       "      <td>13698.554350</td>\n",
       "      <td>293.142870</td>\n",
       "      <td>296.125191</td>\n",
       "      <td>302.061064</td>\n",
       "      <td>288.739608</td>\n",
       "      <td>290.273988</td>\n",
       "      <td>292.107970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537977</td>\n",
       "      <td>0.518776</td>\n",
       "      <td>-0.049587</td>\n",
       "      <td>0.143995</td>\n",
       "      <td>0.356591</td>\n",
       "      <td>0.372768</td>\n",
       "      <td>0.380640</td>\n",
       "      <td>0.392420</td>\n",
       "      <td>-0.074186</td>\n",
       "      <td>7579.469809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>411724</td>\n",
       "      <td>300.297286</td>\n",
       "      <td>14800.494981</td>\n",
       "      <td>13753.180467</td>\n",
       "      <td>291.750152</td>\n",
       "      <td>293.718662</td>\n",
       "      <td>299.219180</td>\n",
       "      <td>287.779530</td>\n",
       "      <td>289.413163</td>\n",
       "      <td>290.932713</td>\n",
       "      <td>...</td>\n",
       "      <td>0.511967</td>\n",
       "      <td>0.563379</td>\n",
       "      <td>0.082507</td>\n",
       "      <td>0.149694</td>\n",
       "      <td>0.389395</td>\n",
       "      <td>0.347135</td>\n",
       "      <td>0.345173</td>\n",
       "      <td>0.381115</td>\n",
       "      <td>-0.079914</td>\n",
       "      <td>5754.140236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>411324</td>\n",
       "      <td>300.250438</td>\n",
       "      <td>14870.302144</td>\n",
       "      <td>13755.946153</td>\n",
       "      <td>291.086794</td>\n",
       "      <td>293.369445</td>\n",
       "      <td>300.014684</td>\n",
       "      <td>287.141116</td>\n",
       "      <td>288.584741</td>\n",
       "      <td>290.356689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.480808</td>\n",
       "      <td>0.460888</td>\n",
       "      <td>-0.102704</td>\n",
       "      <td>0.005744</td>\n",
       "      <td>0.309025</td>\n",
       "      <td>0.317261</td>\n",
       "      <td>0.348503</td>\n",
       "      <td>0.307512</td>\n",
       "      <td>-0.097445</td>\n",
       "      <td>5467.128359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>410122</td>\n",
       "      <td>302.101448</td>\n",
       "      <td>14900.316284</td>\n",
       "      <td>13728.293344</td>\n",
       "      <td>292.494994</td>\n",
       "      <td>295.405081</td>\n",
       "      <td>301.660649</td>\n",
       "      <td>288.002027</td>\n",
       "      <td>289.590761</td>\n",
       "      <td>291.427115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.380461</td>\n",
       "      <td>0.390789</td>\n",
       "      <td>-0.356993</td>\n",
       "      <td>-0.172850</td>\n",
       "      <td>0.260427</td>\n",
       "      <td>0.290878</td>\n",
       "      <td>0.283795</td>\n",
       "      <td>0.284526</td>\n",
       "      <td>-0.095178</td>\n",
       "      <td>6637.200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 66 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NAME  AvgSurfT_inst_chengshu  LST_Day_1km_chousui   \n",
       "0    410522              301.830072         14770.448120  \\\n",
       "1    410421              301.881276         14811.998245   \n",
       "2    411726              298.990822         15035.873561   \n",
       "3    410822              302.326105         14909.855104   \n",
       "4    411082              302.665974         14764.439462   \n",
       "..      ...                     ...                  ...   \n",
       "97   411081              302.379726         14864.959928   \n",
       "98   411424              302.404313         14767.178119   \n",
       "99   411724              300.297286         14800.494981   \n",
       "100  411324              300.250438         14870.302144   \n",
       "101  410122              302.101448         14900.316284   \n",
       "\n",
       "     LST_Night_1km_fennie  soil_temperature_level_2_kaihua   \n",
       "0            13653.260039                       291.679432  \\\n",
       "1            13724.830174                       291.624652   \n",
       "2            13760.252546                       291.141219   \n",
       "3            13684.031442                       291.393057   \n",
       "4            13685.820730                       292.812644   \n",
       "..                    ...                              ...   \n",
       "97           13716.550674                       292.036420   \n",
       "98           13698.554350                       293.142870   \n",
       "99           13753.180467                       291.750152   \n",
       "100          13755.946153                       291.086794   \n",
       "101          13728.293344                       292.494994   \n",
       "\n",
       "     soil_temperature_level_2_guanjiang  soil_temperature_level_2_shouhuo   \n",
       "0                            294.485981                        301.239969  \\\n",
       "1                            294.462918                        301.229017   \n",
       "2                            293.187955                        298.777454   \n",
       "3                            294.128413                        300.920627   \n",
       "4                            295.863324                        302.456445   \n",
       "..                                  ...                               ...   \n",
       "97                           294.927847                        301.419777   \n",
       "98                           296.125191                        302.061064   \n",
       "99                           293.718662                        299.219180   \n",
       "100                          293.369445                        300.014684   \n",
       "101                          295.405081                        301.660649   \n",
       "\n",
       "     soil_temperature_level_3_chousui  soil_temperature_level_3_kaihua   \n",
       "0                          287.263466                       288.780851  \\\n",
       "1                          287.616328                       289.032764   \n",
       "2                          287.204259                       288.763693   \n",
       "3                          286.732262                       288.209019   \n",
       "4                          288.378758                       289.948855   \n",
       "..                                ...                              ...   \n",
       "97                         287.740673                       289.256381   \n",
       "98                         288.739608                       290.273988   \n",
       "99                         287.779530                       289.413163   \n",
       "100                        287.141116                       288.584741   \n",
       "101                        288.002027                       289.590761   \n",
       "\n",
       "     soil_temperature_level_3_guanjiang  ...  SVAI_chousui  SVAI_kaihua   \n",
       "0                            290.517675  ...      0.509428     0.502203  \\\n",
       "1                            290.784441  ...      0.456349     0.444113   \n",
       "2                            290.271875  ...      0.375751     0.406768   \n",
       "3                            290.136585  ...      0.466787     0.392344   \n",
       "4                            291.813767  ...      0.479437     0.456340   \n",
       "..                                  ...  ...           ...          ...   \n",
       "97                           291.034992  ...      0.438036     0.427608   \n",
       "98                           292.107970  ...      0.537977     0.518776   \n",
       "99                           290.932713  ...      0.511967     0.563379   \n",
       "100                          290.356689  ...      0.480808     0.460888   \n",
       "101                          291.427115  ...      0.380461     0.390789   \n",
       "\n",
       "     WDRVI_bajie  WDRVI_chousui  WDVI_bajie  WDVI_yunsui  WDVI_chousui   \n",
       "0      -0.270294       0.038094    0.312649     0.346212      0.380784  \\\n",
       "1      -0.220879       0.020097    0.272293     0.321097      0.313054   \n",
       "2      -0.273682      -0.176015    0.255247     0.291260      0.276982   \n",
       "3      -0.305916       0.009030    0.277992     0.313744      0.335973   \n",
       "4      -0.155182       0.027083    0.329961     0.356682      0.342203   \n",
       "..           ...            ...         ...          ...           ...   \n",
       "97     -0.222630      -0.033628    0.276777     0.307460      0.306785   \n",
       "98     -0.049587       0.143995    0.356591     0.372768      0.380640   \n",
       "99      0.082507       0.149694    0.389395     0.347135      0.345173   \n",
       "100    -0.102704       0.005744    0.309025     0.317261      0.348503   \n",
       "101    -0.356993      -0.172850    0.260427     0.290878      0.283795   \n",
       "\n",
       "     WDVI_kaihua  wet_kaihua           亩产  \n",
       "0       0.355850   -0.090609  7467.000138  \n",
       "1       0.308834   -0.088144  5972.666001  \n",
       "2       0.277341   -0.107184  5687.791498  \n",
       "3       0.327689   -0.062063  7959.597222  \n",
       "4       0.353976   -0.074036  7662.496889  \n",
       "..           ...         ...          ...  \n",
       "97      0.308283   -0.090942  6613.422500  \n",
       "98      0.392420   -0.074186  7579.469809  \n",
       "99      0.381115   -0.079914  5754.140236  \n",
       "100     0.307512   -0.097445  5467.128359  \n",
       "101     0.284526   -0.095178  6637.200003  \n",
       "\n",
       "[102 rows x 66 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75eb75d7-b0ce-4b35-98b5-91a2ab2470aa",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 数据处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bb20839c-6340-44b1-a9e7-3c0c367273c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############删除缺失率高于05的特征\n",
    "null_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        null_list.append([col,d[col].isnull().sum() / d.shape[0]])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "null_df = pd.DataFrame(null_list,columns = ['特征名称','缺失率'])\n",
    "del_cols = null_df['特征名称'][null_df['缺失率'] >= 0.5]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aba9ae28-97f8-4fec-bc66-dd5fbfd318ec",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "删除特征的个数： 0\n",
      "出错特征的个数： 0\n",
      "删除的特征： []\n"
     ]
    }
   ],
   "source": [
    "#############表格中用绿色标记表示删除同值比例高的特征\n",
    "tz_list = []\n",
    "error_list = []\n",
    "for col in d.columns:\n",
    "    try:\n",
    "        tz_list.append([col,d[d[col] == d[col].mode()[0]].shape[0] / (d.shape[0] - d[col].isnull().sum())])\n",
    "    except:\n",
    "        error_list.append(col)\n",
    "tz_df = pd.DataFrame(tz_list,columns = ['特征名称','同值比例'])\n",
    "del_cols = tz_df['特征名称'][tz_df['同值比例'] >= 0.9]\n",
    "print('删除特征的个数：',len(del_cols))\n",
    "print('出错特征的个数：',len(error_list))\n",
    "print('删除的特征：',del_cols.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4032c90c-2e9f-47ff-9b43-383d7419a1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=d.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo','AvgSurfT_inst_chengshu','soil_temperature_level_2_guanjiang', 'soil_temperature_level_3_guanjiang', \n",
    "                  'soil_temperature_level_1_guanjiang', 'SoilMoi0_10cm_inst_guanjiang', 'SoilMoi10_40cm_inst_guanjiang',\n",
    "                  'soil_temperature_level_2_kaihua', 'soil_temperature_level_3_kaihua', 'SoilMoi0_10cm_inst_kaihua', 'SoilMoi10_40cm_inst_kaihua',\n",
    "                  'DVI_kaihua', 'EVI_kaihua', 'EVI2_kaihua', 'GCVI_kaihua', 'Lai_500m_kaihua', 'MCARI_kaihua', 'OSAVI_kaihua', \n",
    "                  'SR_kaihua', 'SVAI_kaihua', 'WDVI_kaihua', 'wet_kaihua','LST_Day_1km_chousui', 'soil_temperature_level_3_chousui', \n",
    "                  'SoilMoi0_10cm_inst_chousui', 'SoilMoi10_40cm_inst_chousui', 'DVI_chousui', 'EVI_chousui', 'EVI2_chousui', \n",
    "                   'Lai_500m_chousui', 'MCARI_chousui', 'MSR_chousui', 'WDRVI_chousui', 'WDVI_chousui','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui'])\n",
    "                  \n",
    "dd=dd.drop(columns=['GNDVI_kaihua','NDVI_chousui','OSAVI_chousui',\n",
    "                   'SVAI_chousui','SR_chousui','NAME','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui','NAME','soil_temperature_level_2_shouhuo',\n",
    "                  'VDVI_shouhuo','AvgSurfT_inst_chengshu','soil_temperature_level_2_guanjiang', 'soil_temperature_level_3_guanjiang', \n",
    "                  'soil_temperature_level_1_guanjiang', 'SoilMoi0_10cm_inst_guanjiang', 'SoilMoi10_40cm_inst_guanjiang',\n",
    "                  'soil_temperature_level_2_kaihua', 'soil_temperature_level_3_kaihua', 'SoilMoi0_10cm_inst_kaihua', 'SoilMoi10_40cm_inst_kaihua',\n",
    "                  'DVI_kaihua', 'EVI_kaihua', 'EVI2_kaihua', 'GCVI_kaihua', 'Lai_500m_kaihua', 'MCARI_kaihua', 'OSAVI_kaihua', \n",
    "                  'SR_kaihua', 'SVAI_kaihua', 'WDVI_kaihua', 'wet_kaihua','LST_Day_1km_chousui', 'soil_temperature_level_3_chousui', \n",
    "                  'SoilMoi0_10cm_inst_chousui', 'SoilMoi10_40cm_inst_chousui', 'DVI_chousui', 'EVI_chousui', 'EVI2_chousui', \n",
    "                   'Lai_500m_chousui', 'MCARI_chousui', 'MSR_chousui', 'WDRVI_chousui', 'WDVI_chousui','DVI_yunsui', \n",
    "                  'EVI_yunsui', 'EVI2_yunsui', 'GCVI_yunsui', 'Lai_500m_yunsui', 'MCARI_yunsui', 'MSR_yunsui', 'NDVI_yunsui', \n",
    "                  'OSAVI_yunsui', 'SVAI_yunsui', 'WDVI_yunsui'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4a117973-8634-4679-8251-f818d42ab8c0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#d=d.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])\n",
    "#dd=dd.drop(columns=['GNDVI_chousui','SR_yunsui','WDRVI_chousui','SVAI_chousui','SR_chousui'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "299d2aaa-7024-4510-a82e-7a8c7e2bce87",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 全部入模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fc106ad-358a-451e-bbdf-57729747d20a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x_train =d.drop(columns = [\"亩产\"])\n",
    "y_train = d['亩产']\n",
    "x_test =dd.drop(columns = [\"亩产\"])\n",
    "y_test = dd['亩产']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef9e4a27-8669-49ea-b699-edba291bb268",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = xgb.XGBRegressor(n_estimators=150,\n",
    "                                 max_depth=5,\n",
    "                                 learning_rate=0.1,\n",
    "                                 \n",
    "                                 random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "44180510-e0eb-449d-bc96-8b985e0ca0c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xgboost_model_fit = model.fit(x_train, y_train)\n",
    "y_pred_bo = xgboost_model_fit.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "136d8f93-7a6c-4694-bbb0-cbc45073abd5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>平均绝对误差（MAPE）</th>\n",
       "      <th>平均绝对误差（MAE）</th>\n",
       "      <th>均方根误差（RMSE）</th>\n",
       "      <th>决定系数（R^2）</th>\n",
       "      <th>解释方差</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>0.103255</td>\n",
       "      <td>621.763421</td>\n",
       "      <td>791.45728</td>\n",
       "      <td>0.652333</td>\n",
       "      <td>0.675926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         平均绝对误差（MAPE）  平均绝对误差（MAE）  均方根误差（RMSE）  决定系数（R^2）      解释方差\n",
       "xgboost      0.103255   621.763421    791.45728   0.652333  0.675926"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均绝对百分比误差\n",
    "mape=mean_absolute_percentage_error(y_test,y_pred_bo)\n",
    "# 计算均方根误差（RMSE）\n",
    "rmse = mean_squared_error(y_test, y_pred_bo, squared=False)\n",
    "# 计算平均绝对误差（MAE）\n",
    "mae = mean_absolute_error(y_test, y_pred_bo)\n",
    "# 计算决定系数（R^2）\n",
    "r2 = r2_score(y_test, y_pred_bo)\n",
    "# 计算解释方差分\n",
    "explained_var = explained_variance_score(y_test, y_pred_bo)\n",
    "\n",
    "results1 = pd.DataFrame()\n",
    "results1['平均绝对误差（MAPE）'] = [mape]\n",
    "results1['平均绝对误差（MAE）'] = [mae]\n",
    "results1['均方根误差（RMSE）'] = [rmse]\n",
    "results1['决定系数（R^2）'] = [r2]\n",
    "results1['解释方差'] = [explained_var]\n",
    "results1.index = ['xgboost']\n",
    "results1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "43a37742-b0a1-4ac8-9f09-39d1fe7992bf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>precent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7467.000138</td>\n",
       "      <td>6651.978516</td>\n",
       "      <td>-10.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5972.666001</td>\n",
       "      <td>6213.860352</td>\n",
       "      <td>4.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5687.791498</td>\n",
       "      <td>5323.748535</td>\n",
       "      <td>-6.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7959.597222</td>\n",
       "      <td>7178.595215</td>\n",
       "      <td>-9.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7662.496889</td>\n",
       "      <td>6800.393066</td>\n",
       "      <td>-11.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>6613.422500</td>\n",
       "      <td>6327.526855</td>\n",
       "      <td>-4.32%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>7579.469809</td>\n",
       "      <td>7304.106445</td>\n",
       "      <td>-3.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>5754.140236</td>\n",
       "      <td>6921.090332</td>\n",
       "      <td>20.28%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5467.128359</td>\n",
       "      <td>6472.301270</td>\n",
       "      <td>18.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>6637.200003</td>\n",
       "      <td>6424.547852</td>\n",
       "      <td>-3.20%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          y_test       y_pred  precent\n",
       "0    7467.000138  6651.978516  -10.91%\n",
       "1    5972.666001  6213.860352    4.04%\n",
       "2    5687.791498  5323.748535   -6.40%\n",
       "3    7959.597222  7178.595215   -9.81%\n",
       "4    7662.496889  6800.393066  -11.25%\n",
       "..           ...          ...      ...\n",
       "97   6613.422500  6327.526855   -4.32%\n",
       "98   7579.469809  7304.106445   -3.63%\n",
       "99   5754.140236  6921.090332   20.28%\n",
       "100  5467.128359  6472.301270   18.39%\n",
       "101  6637.200003  6424.547852   -3.20%\n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_array = np.array(y_test)\n",
    "\n",
    "y_pred_series = pd.Series(y_pred_bo, name='y_pred')\n",
    "y_test_series = pd.Series(y_test_array, name='y_test')\n",
    "dftest= pd.concat([y_test_series, y_pred_series], axis=1)\n",
    "dftest['precent'] = ((dftest['y_pred'] - dftest['y_test']) / dftest['y_test']) * 100\n",
    "dftest['precent'] = dftest['precent'].apply(lambda x: '{:.2f}%'.format(x))\n",
    "dftest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b1994e2-af3b-478a-992d-e4128e188f91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_test_sorted = np.sort(y_test)\n",
    "y_pred_sorted = y_pred_bo[np.argsort(y_test)]\n",
    "\n",
    "# 绘制折线对比图\n",
    "plt.plot(range(len(y_test_sorted)), y_test_sorted, label='Actual')\n",
    "plt.plot(range(len(y_pred_sorted)), y_pred_sorted, label='Predicted',linestyle='--')\n",
    "#plt.xlabel('Index')\n",
    "plt.ylabel('Value',fontsize=16)\n",
    "plt.title('Actual vs. Predicted (Bo-XGBoost)',fontsize=16)\n",
    "plt.legend()\n",
    "plt.ylim(1000, 10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df4a379a-10bb-4f49-8b5f-1490cbda5d25",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.scatter(y_test, y_pred_bo, alpha=0.5, label='Data Points')\n",
    "plt.xlabel('Actual Values')\n",
    "plt.ylabel('Predicted Values')\n",
    "plt.title('bh-XGBoost Scatter Plot of Actual vs. Predicted Values')\n",
    "\n",
    "# 添加对角线\n",
    "max_value = max(max(y_test), max(y_pred_bo))\n",
    "min_value = min(min(y_test), min(y_pred_bo))\n",
    "plt.plot([min_value, max_value], [min_value, max_value], color='red', linestyle='--', linewidth=2, label='Perfect Prediction')\n",
    "\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3896133d-f939-4563-8d25-5aed0e0b5413",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
